REAGO
Reago: Targeted 16S rRNA Gene Assembler for Metagenomic Data
Reago assembles 16S rRNA genes from metagenomic sequencing data using a targeted approach optimized for phylogenetic marker gene reconstruction under high sequence similarity, uneven microbial abundance, large dataset volume, and absence of reference sequences.
Key Features:
- Targeted 16S rRNA Assembly: Specifically reconstructs 16S rRNA genes rather than performing generic de novo metagenomic assembly.
- Secondary Structure-Aware Homology Search: Incorporates rRNA secondary structure information into homology searches to improve reconstruction accuracy.
- rRNA Gene Property Utilization: Leverages intrinsic structural and sequence properties of 16S rRNA genes to enhance assembly performance.
- High-Complexity Dataset Handling: Addresses challenges of high sequence similarity among related microbes, skewed species abundance, large metagenomic datasets, and missing reference genes.
- Improved Gene Recovery: Demonstrates higher accuracy in recovering 16S rRNA genes compared to generic metagenomic assemblers and other rRNA reconstruction tools.
Scientific Applications:
- Microbial Community Analysis: Enables accurate reconstruction of 16S rRNA genes for taxonomic profiling, diversity assessment, and phylogenetic analysis in microbial ecology studies.
- Environmental Metagenomics: Supports analysis of complex environmental samples lacking comprehensive reference genomes.
Methodology:
Reago applies a targeted assembly strategy focused on 16S rRNA genes, combining secondary structure-aware homology searches with exploitation of rRNA-specific sequence and structural properties to reconstruct full-length genes from complex metagenomic datasets.
Topics
Details
- Tool Type:
- command-line tool
- Operating Systems:
- Linux
- Programming Languages:
- Python
- Added:
- 8/3/2017
- Last Updated:
- 11/24/2024
Operations
Publications
Yuan C, Lei J, Cole J, Sun Y. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics. 2015;31(12):i35-i43. doi:10.1093/bioinformatics/btv231. PMID:26072503. PMCID:PMC4765874.